Time Slot Detection‐Based <i>M</i>‐ary Tree Anticollision Identification Protocol for RFID Tags in the Internet of Things

Yang, Xiaojiao; Wu, Bizao; Wu, Shixun; Liu, Xinxin; Zhao, Wancheng

doi:10.1155/2021/6638936

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Anti-collision algorithms are divided into two categories: the tree-based algorithm [ 17 , 18 , 19 ] (including query tree and tree splitting) and the ALOHA-based algorithm [ 20 , 21 , 22 , 23 ]. The former requires high computing costs when solving the problem of tag starvation.…”

Section: Introductionmentioning

confidence: 99%

Capture-Aware Dense Tag Identification Using RFID Systems in Vehicular Networks

Xu,

Song,

Sun

et al. 2023

Sensors

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Passive radio-frequency identification (RFID) systems have been widely applied in different fields, including vehicle access control, industrial production, and logistics tracking, due to their ability to improve work quality and management efficiency at a low cost. However, in an intersection situation where tags are densely distributed with vehicle gathering, the wireless channel becomes extremely complex, and the readers on the roadside may only decode the information from the strongest tag due to the capture effect, resulting in tag misses and considerably reducing the performance of tag identification. Therefore, it is crucial to design an efficient and reliable tag-identification algorithm in order to obtain information from vehicle and cargo tags under adverse traffic conditions, ensuring the successful application of RFID technology. In this paper, we first establish a Nakagami-m distributed channel capture model for RFID systems and provide an expression for the capture probability, where each channel is modeled as any relevant Nakagami-m distribution. Secondly, an advanced capture-aware tag-estimation scheme is proposed. Finally, extensive Monte Carlo simulations show that the proposed algorithm has strong adaptability to circumstances for capturing under-fading channels and outperforms the existing algorithms in terms of complexity and reliability of tag identification.

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Section: Introductionmentioning

confidence: 99%

Capture-Aware Dense Tag Identification Using RFID Systems in Vehicular Networks

Xu,

Song,

Sun

et al. 2023

Sensors

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ZenseTag: An RFID assisted Twin-Tag Single Antenna COTS Sensor Interface

Bhat,

Gupta,

Bansal

et al. 2024

Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems

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Research on Intelligent Warehousing and Logistics Management System of Electronic Market Based on Machine Learning

Zhang¹,

Zhou²,

Jin³

et al. 2022

Computational Intelligence and Neuroscience

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This study applies the Internet of things information-aware technology to the process of electronic market warehousing and logistics management, effectively perceives warehouse electronic product logistics information, and improves the real-time perception of electronic product logistics information and the efficiency of electronic product storage logistics management. This study first analyzes the needs of the intelligent electronic market warehouse logistics management system and then builds the IoT architecture of the intelligent warehouse logistics assembly logistics management system for electronic warehouses based on machine learning algorithms, which solves the problems that exist in the current workshop electronic market warehouse logistics management. Then, the principle of RFID technology is introduced. The accuracy of RFID tag estimation is analyzed by the PEPC tag estimation algorithm. It is concluded that the PEPC tag estimation algorithm reduces the tag estimation error and improves the accuracy of tag estimation. Finally, an intelligent warehousing logistics management system based on IoT RFID technology is established. The test results show that the system can meet the requirements of intelligent warehousing function in the electronic market, which will greatly improve the warehousing efficiency of electronic products.

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Time Slot Detection‐Based M‐ary Tree Anticollision Identification Protocol for RFID Tags in the Internet of Things

Cited by 3 publications

References 29 publications

Capture-Aware Dense Tag Identification Using RFID Systems in Vehicular Networks

Capture-Aware Dense Tag Identification Using RFID Systems in Vehicular Networks

ZenseTag: An RFID assisted Twin-Tag Single Antenna COTS Sensor Interface

Research on Intelligent Warehousing and Logistics Management System of Electronic Market Based on Machine Learning

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